Electrical Machines Diagnosis

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ISBN: 9781848212633 | 1848212631
Cover: Hardcover
Copyright: 9/13/2011

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Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives. This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit. Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations. Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers.

Preface	p. xi
Faults in Electrical Machines and their Diagnosis	p. 1
Introduction	p. 1
Composition of induction machines	p. 3
The stator	p. 4
The rotor	p. 4
Bearings	p. 5
Failures in induction machines	p. 5
Mechanical failures	p. 8
Electrical failures	p. 9
Overview of methods for diagnosing induction machines	p. 10
Diagnosis methods using an analytical model	p. 12
Diagnostic methods with no analytical model	p. 16
Conclusion	p. 18
Bibliography	p. 19
Modeling Induction Machine Winding Faults for Diagnosis	p. 23
Introduction	p. 23
Simulation model versus diagnosis model	p. 23
Objectives	p. 24
Methodology	p. 24
Chapter structure	p. 25
Study framework and general methodology	p. 26
Working hypotheses	p. 26
Equivalence between winding systems	p. 27
Equivalent two-phase machine with no fault	p. 34
Consideration of a stator winding fault	p. 37
Model of the machine with a stator insulation fault	p. 40
Electrical equations of the machine with a stator short-circuit	p. 40
State model in any reference frame	p. 43
Extension of the three-phase stator model	p. 47
Model validation	p. 48
Generalization of the approach to the coupled modeling of stator and rotor faults	p. 51
Electrical equations in the presence of rotor imbalance	p. 53
Generalized model of the machine with stator and rotor faults	p. 55
Methodology for monitoring the induction machine	p. 57
Parameter estimation for induction machine diagnosis	p. 58
Experimental validation of the monitoring strategy	p. 61
Conclusion	p. 64
Bibliography	p. 67
Closed-Loop Diagnosis of the Induction Machine	p. 69
Introduction	p. 69
Closed-loop identification	p. 71
Problems in closed-loop identification	p. 71
Identification problems for diagnosing electrical machines	p. 73
General methodology of closed-loop identification of induction machine	p. 74
Taking control into account	p. 74
Machine identification by closed-loop decomposition	p. 76
Identification results	p. 80
Closed-loop diagnosis of simultaneous stator/rotor faults	p. 82
General model of induction machine faults	p. 82
Parameter estimation with a priori information	p. 83
Detection and localization	p. 84
Comparison of identification results through direct and indirect approaches	p. 87
Conclusion	p. 89
Bibliography	p. 90
Induction Machine Diagnosis Using Observers	p. 93
Introduction	p. 93
Model presentation	p. 96
Three-phase model of induction machine without fault	p. 96
Park's model of an induction machine without fault	p. 100
Induction machine models with fault	p. 104
Observers	p. 104
Principle	p. 104
Different kinds of observers	p. 108
Extended observer	p. 115
Applying observers to diagnostics	p. 119
Using Park's model	p. 119
Use of the three-phase model	p. 124
Spectral analysis of the torque reconstructed by the observer	p. 125
Conclusion	p. 127
Bibliography	p. 128
Thermal Monitoring of the Induction Machine	p. 131
Introduction	p. 131
Aims of the thermal monitoring on induction machines	p. 131
Main methods of thermal monitoring of the induction machines	p. 133
Real-time parametric estimation by Kalman filter	p. 137
Interest and specificities of the Kalman filter	p. 137
Implementation of an extended Kalman filter	p. 138
Electrical models for the thermal monitoring	p. 142
Continuous time models	p. 143
Full-order model	p. 144
Discretized and extended model	p. 147
Experimental system	p. 149
General presentation of the test bench	p. 149
Thermal instrumentation	p. 151
Electrical instrumentation	p. 153
Experimental results	p. '57
Tuning of the Kalman filter	p. 157
Influence of the magnetic saturation	p. 160
Conclusion	p. 162
Appendix: induction machine characteristics	p. 163
Bibliography	p. 163
Diagnosis of the Internal Resistance of an Automotive Lead-acid Battery by the Implementation of a Model Invalidation-based Approach: Application to Crankability Estimation	p. 167
Introduction	p. 167
Fractional model of a lead-acid battery for the start-up phase	p. 169
Identification of the fractional model	p. 171
Output error identification algorithm	p. 171
Calculation of the output sensitivities	p. 173
Validation of the estimated parameters	p. 174
Application to start-up signals	p. 174
Battery resistance as crankability estimator	p. 175
Model validation and estimation of the battery resistance	p. 178
Frequency approach of the model validation	p. 178
Application to the estimation of the battery resistance	p. 181
Simplified resistance estimator	p. 184
Toward a battery state estimator	p. 188
Conclusion	p. 188
Bibliography	p. 190
Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis	p. 193
Introduction	p. 193
The spectrum of the current line	p. 194
Signal processing	p. 196
Fourier's transform	p. 196
Periodogram	p. 197
Signal analysis from experiment campaigns	p. 199
Disturbances induced by a broken bar	p. 199
Bearing faults	p. 205
Static eccentricity	p. 211
Inter turn short circuits	p. 220
Conclusion	p. 222
Appendices	p. 223
Appendix A	p. 223
Appendix B	p. 223
Bibliography	p. 224
Fault Diagnosis of the Induction Machine by Neural Networks	p. 227
Introduction	p. 227
Methodology of the use of the ANN in the diagnostic domain	p. 228
Choice of the fault indicators	p. 229
Choice of the structure of the network	p. 230
Construction of the learning and test base	p. 231
Learning and test of the network	p. 232
Description of the monitoring system	p. 232
The detection problem	p. 233
The proposed method for the robust detection	p. 235
Generation of the estimated residues	p. 236
Signature of the stator and rotor faults	p. 237
Analysis of the residue in healthy regime	p. 237
Analysis of the residue in presence of the stator fault	p. 237
Analysis of the residue in presence of the rotor fault	p. 241
Analysis of the residue in presence of simultaneous stator/rotor fault	p. 244
Detection of the faults by the RN_d neural network	p. 244
Extraction of the fault indicators	p. 244
Learning sequence of the RN_d network	p. 245
Structure of the RN_d network	p. 246
Results of the learning of the RN_d network	p. 247
Test results of the RN_d network	p. 248
Diagnosis of the stator fault	p. 251
Choice of the fault indicators for the RN_CC network	p. 251
Learning sequence of the RN_CC network	p. 253
Structure of the RN_CC network	p. 254
Learning results of the RN_CC network	p. 255
Results of the test of the RN_CC network	p. 256
Experimental validation of the RN_CC network	p. 259
Diagnosis of the rotor fault	p. 263
Choice of the fault indicators of the RN_bC network	p. 265
Learning sequence of the RN_bC network	p. 265
Learning, test and validation results	p. 266
Complete monitoring system of the induction machine	p. 267
Conclusion	p. 268
Bibliography	p. 269
Faults Detection and Diagnosis in a Static Converter	p. 271
Introduction	p. 271
Detection and diagnosis	p. 273
Neural network approach	p. 273
A fuzzy logic approach	p. 280
Multi-dimensional data analysis	p. 285
Thermal fatigue of power electronic moduli and failure modes	p. 294
Presentation of power electronic moduli in diagnosis	p. 294
Causes and main types of degradation of power electronics moduli	p. 304
Interconnection degradation effects on electrical characteristics and potential use for diagnosis	p. 310
Effects of interface degradation on thermal characteristics and potential use for diagnosis	p. 313
Conclusion	p. 316
Bibliography	p. 316
List of Authors	p. 321
Index	p. 327
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